Computer vision applications for urban planning: A systematic review of opportunities and constraints

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

“…AI encompasses a wide variety of models and technologies, including ML, DL, CV, NLP, Evolutionary Computing (EC), and robotics. These models have been applied to various urban planning and design functions [ [1] , [2] , [3] , [4] , [5] , [6] ] in the context of environmental sustainability in sustainable smart cities [ 7 , 8 , 21 , 22 , [24] , [27] , [66] , [67] ]. The integration of AI technologies into urban planning and design processes underscores the pivotal role of AI in advancing the development of sustainable smart cities.…”

“…To gain a better understanding of how CV can be integrated into the urban planning process to create more sustainable smart cities, Marasinghe et al. [ 3 ] conducted a systematic review of CV applications in urban planning, highlighting the opportunities offered by CV to support various planning tasks while acknowledging existing challenges. Their findings unveil several key points: (a) CV has the potential to facilitate numerous urban planning tasks, spanning from data collection and analysis to issue identification and prioritization, public participation, plan design and adoption, and implementation and evaluation; (b) CV can enhance decision-making processes through diverse visual information, though its limitations must be taken into account; and (c) CV utilization in urban planning holds promise for advancing efforts in sustainable urban development.…”

“…Additionally, applications of ML for CV tasks in the domain of urban planning and design include exploring associations between people's urban density and urban characteristics [ 132 ] and analyzing associations between eye-level urban design quality and on-street crime density [ 133 ]. Urban planners and policymakers can gain valuable insights and empirical evidence to guide decisions in sustainable urban planning and design by utilizing CV techniques to investigate and assess the spatial correlation and underlying factors contributing to diverse urban challenges and prospects [ 3 ]. The emergence of spatial-explicit geospatial AI (GeoAI), which integrates spatial attributes and reasoning into AI computations, is increasingly prevalent in urban studies [ 134 , 135 ], offering diverse applications and opportunities for environmentally sustainable urban planning and development.…”

“…The convergence of AI, AIoT, UDT, data-driven urban planning, and environmental sustainability has emerged as a critical frontier in the development of sustainable smart cities. In recent years, AI and AIoT have significantly transformed data-driven urban planning practices, as evidenced by notable research contributions in the field [ [1] , [2] , [3] , [4] , [5] , [6] ]. This integrated approach highlights the synergistic potential of AI and AIoT technologies in reshaping urban planning strategies to address complex environmental challenges.…”

LighterFace Model for Community Face Detection and Recognition

“…Artificial intelligence-based computer vision strategies have proven to be efficient and widely deployed in numerous communities [3], particularly with high-coverage cameras [4]. However, challenges can arise due to the limited computational power of the built-in chips in these cameras, driven by economic cost considerations.…”